An integrated indicator framework for spatial assessment of industrial and social vulnerability to indirect disaster losses

The focus of this study is on multi-dimensional vulnerability of regions to indirect disaster losses. An integrated indicator framework has been developed which captures the multi-layered vulnerability drivers in industrial production systems and also accounts for the social fragilities and coping capacities in communities. By combining industrial vulnerability and social vulnerability spatially, and proposing a methodology to account between their interactions, the total vulnerability to indirect risks of regions is revealed. The outcome of the framework is a ranking of industrial sectors and geographic areas according to their vulnerability against indirect losses. It answers the question which of the two affected regions is in a better position to cope with indirect consequences in a disaster. Indicators provide a flexible framework for the comparison and integration of different data types and allow the combination of social as well as economic aspects. Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was applied to analyze direct and indirect dependencies within the selected social and industrial vulnerability indicators. The hierarchical indicator system has been implemented in a software system based on multi-criteria decision theory (MCDA) with an interactive interface to take into account a broader range of expert judgement. The methodology was applied in a case study in the state of Baden-Wuerttemberg in Germany for 16 different industrial sectors. The approach helps to identify particular vulnerable processes and points out where mitigation measures could be implemented most effectively.     

The fingerprint of global warming in the Tropical Pacific

正The impacts of global warming will be felt most strongly at regional scales.However,great uncertainties exist in climate change projections at these scales,limiting our ability to provide useful information for the planning and implementation of appropriate adaptation measures.Thus,there is an urgent need to reduce these uncertainties.     

A simple model perturbed physics study of the simulated climate sensitivity uncertainty and its relation to control climate biases

In this study the relationship between climate model biases in the control climate and the simulated climate sensitivity are discussed on the basis of perturbed physics ensemble simulations with a globally resolved energy balance (GREB) model. It is illustrated that the uncertainties in the simulated climate sensitivity (estimated by the transient response to CO₂ forcing scenarios in the twenty first century or idealized 2 × CO₂ forcing experiments) can be conceptually split into two parts: a direct effect of the perturbed physics on the climate sensitivity independent of the control mean climate and an indirect effect of the perturbed physics by changing the control mean climate, which in turn changes the climate sensitivity, as the climate sensitivity itself is depending on the control climate. Biases in the control climate are negatively correlated with the climate sensitivity (colder climates have larger sensitivities), if no physics are perturbed. Perturbed physics that lead to warmer control climate, would in average also lead to larger climate sensitivities, if the control climate is held at the observed reference climate by flux corrections. Thus the effects of control biases and perturbed physics are opposing each other and are partially cancelling each other out. In the GREB model the biases in the control climate are the more important effect for the regional climate sensitivity uncertainties, but for the global mean climate sensitivity both, the biases in the control climate and the perturbed physics, are equally important.     

An objective analysis of the observed spatial structure of the tropical Indian Ocean SST variability

The observed interannual Indian Ocean sea surface temperature (SST) variability from 1950 to 2008 is analyzed in respect to the spatial structure of the variability. The analysis is based on an objective comparison of the leading empirical orthogonal function modes against the stochastic null hypothesis of spatial red noise (isotropic diffusion). Starting from this red noise assumption, the analysis searches for those structures that are most distinct from the red noise hypothesis. This objective approach will put previously well and less known modes of variability into the context of the multivariate SST variability. The Indian Ocean SST variability is marked by relatively weak SST variability, which is strongly dominated by a basin wide monopole pattern that is caused by different processes. The leading modes of variability are the El Nino Southern Oscillation (ENSO) variability and the warming trend, which both project onto the basin wide monopole structure. Other more characteristic spatial patterns of internal variability are much less dominant in the tropical Indian Ocean, which is quite different from all other ocean basin, where characteristic teleconnection patterns exist. The remaining, ENSO independent, detrended variability is dominated by multi-pole patterns from the southern Indian Ocean reaching into the tropical Indian Ocean, which are probably primarily caused by extra-tropical atmospheric forcings. The large scale tropical Indian Ocean internal variability itself has no dominant structure. The currently often used dipole mode index (DMI) does not appear to present a dominant teleconnection pattern of the Indian Ocean internal SST variability. In the context of the objective analysis presented here, the DMI partly reflects the ENSO variability and is also a representation of the multi-dimensional, chaotic spatial red noise (isotropic diffusion) process. As such the DMI cannot be interpreted as a coherent teleconnection between the two poles.